{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transfer Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transfer learning refers to the process of pre-training a flexible model on a large dataset and using it later on other data with little to no training. It is one of the most outstanding 🚀 achievements in Machine Learning 🧠 and has many practical applications.\n",
    "\n",
    "For time series forecasting, the technique allows you to get lightning-fast predictions ⚡ bypassing the tradeoff between accuracy and speed (more than 30 times faster than our alreadsy fast [autoARIMA](https://github.com/Nixtla/statsforecast) for a similar accuracy).\n",
    "\n",
    "This notebook shows how to generate a pre-trained model and store it in a checkpoint to make it available to forecast new time series never seen by the model. \n",
    "\n",
    "Table of Contents<br>\n",
    "1.   Installing NeuralForecast/DatasetsForecast<br>\n",
    "2.   Load M4 Data<br>\n",
    "3.   Instantiate NeuralForecast core, Fit, and save<br>\n",
    "4.   Load pre-trained model and predict on AirPassengers<br>\n",
    "5.   Evaluate Results<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run these experiments using GPU with Google Colab.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/Nixtla/neuralforecast/blob/main/nbs/examples/Transfer_Learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Installing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install datasetsforecast neuralforecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "from datasetsforecast.m4 import M4\n",
    "from neuralforecast.core import NeuralForecast\n",
    "from neuralforecast.models import NHITS\n",
    "from neuralforecast.utils import AirPassengersDF\n",
    "from utilsforecast.losses import mae\n",
    "from utilsforecast.plotting import plot_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.getLogger(\"pytorch_lightning\").setLevel(logging.WARNING)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example will automatically run on GPUs if available. **Make sure** cuda is available. (If you need help to put this into production send us an email or join or community, we also offer a fully hosted solution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load M4 Data\n",
    "\n",
    "The `M4` class will automatically download the complete M4 dataset and process it.\n",
    "\n",
    "It return three Dataframes: `Y_df` contains the values for the target variables, `X_df` contains exogenous calendar features and `S_df` contains static features for each time-series (none for M4). For this example we will only use `Y_df`.\n",
    "\n",
    "If you want to use your own data just replace `Y_df`. Be sure to use a long format and have a simmilar structure than our data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1</td>\n",
       "      <td>1970-01-01 00:00:00.000000001</td>\n",
       "      <td>8000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1</td>\n",
       "      <td>1970-01-01 00:00:00.000000002</td>\n",
       "      <td>8350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1</td>\n",
       "      <td>1970-01-01 00:00:00.000000003</td>\n",
       "      <td>8570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1</td>\n",
       "      <td>1970-01-01 00:00:00.000000004</td>\n",
       "      <td>7700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1</td>\n",
       "      <td>1970-01-01 00:00:00.000000005</td>\n",
       "      <td>7080.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11246406</th>\n",
       "      <td>M9999</td>\n",
       "      <td>1970-01-01 00:00:00.000000083</td>\n",
       "      <td>4200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11246407</th>\n",
       "      <td>M9999</td>\n",
       "      <td>1970-01-01 00:00:00.000000084</td>\n",
       "      <td>4300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11246408</th>\n",
       "      <td>M9999</td>\n",
       "      <td>1970-01-01 00:00:00.000000085</td>\n",
       "      <td>3800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11246409</th>\n",
       "      <td>M9999</td>\n",
       "      <td>1970-01-01 00:00:00.000000086</td>\n",
       "      <td>4400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11246410</th>\n",
       "      <td>M9999</td>\n",
       "      <td>1970-01-01 00:00:00.000000087</td>\n",
       "      <td>4300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11246411 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         unique_id                            ds       y\n",
       "0               M1 1970-01-01 00:00:00.000000001  8000.0\n",
       "1               M1 1970-01-01 00:00:00.000000002  8350.0\n",
       "2               M1 1970-01-01 00:00:00.000000003  8570.0\n",
       "3               M1 1970-01-01 00:00:00.000000004  7700.0\n",
       "4               M1 1970-01-01 00:00:00.000000005  7080.0\n",
       "...            ...                           ...     ...\n",
       "11246406     M9999 1970-01-01 00:00:00.000000083  4200.0\n",
       "11246407     M9999 1970-01-01 00:00:00.000000084  4300.0\n",
       "11246408     M9999 1970-01-01 00:00:00.000000085  3800.0\n",
       "11246409     M9999 1970-01-01 00:00:00.000000086  4400.0\n",
       "11246410     M9999 1970-01-01 00:00:00.000000087  4300.0\n",
       "\n",
       "[11246411 rows x 3 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_df, _, _ = M4.load(directory='./', group='Monthly', cache=True)\n",
    "Y_df['ds'] = pd.to_datetime(Y_df['ds'])\n",
    "Y_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Model Train and Save\n",
    "\n",
    "Using the `NeuralForecast.fit` method you can train a set of models to your dataset. You just have to define the `input_size` and `horizon` of your model. The `input_size` is the number of historic observations (lags) that the model will use to learn to predict `h` steps in the future. Also, you can modify the hyperparameters of the model to get a better accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lightning_fabric.utilities.seed:Seed set to 1\n"
     ]
    }
   ],
   "source": [
    "horizon = 12\n",
    "stacks = 3\n",
    "models = [NHITS(input_size=5 * horizon,\n",
    "                h=horizon,\n",
    "                max_steps=100,\n",
    "                stack_types = stacks*['identity'],\n",
    "                n_blocks = stacks*[1],\n",
    "                mlp_units = [[256,256] for _ in range(stacks)],\n",
    "                n_pool_kernel_size = stacks*[1],\n",
    "                batch_size = 32,\n",
    "                scaler_type='standard',\n",
    "                n_freq_downsample=[12,4,1],\n",
    "                enable_progress_bar=False,\n",
    "                interpolation_mode=\"nearest\",\n",
    "               )]\n",
    "nf = NeuralForecast(models=models, freq='ME')\n",
    "nf.fit(df=Y_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save model with `core.NeuralForecast.save` method. This method uses PytorchLightning `save_checkpoint` function. We set `save_dataset=False` to only save the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nf.save(path='./results/transfer/', model_index=None, overwrite=True, save_dataset=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Transfer M4 to AirPassengers\n",
    "\n",
    "We load the stored model with the `core.NeuralForecast.load` method, and forecast `AirPassenger` with the `core.NeuralForecast.predict` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Nixtla\\Repositories\\neuralforecast\\neuralforecast\\common\\_base_model.py:133: UserWarning: NHITS is a univariate model. Parameter n_series is ignored.\n",
      "  warnings.warn(\n",
      "INFO:lightning_fabric.utilities.seed:Seed set to 1\n"
     ]
    }
   ],
   "source": [
    "fcst2 = NeuralForecast.load(path='./results/transfer/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We define the train df. \n",
    "Y_df = AirPassengersDF.copy()\n",
    "mean = Y_df[Y_df.ds<='1959-12-31']['y'].mean()\n",
    "std = Y_df[Y_df.ds<='1959-12-31']['y'].std()\n",
    "\n",
    "Y_train_df = Y_df[Y_df.ds<='1959-12-31'] # 132 train\n",
    "Y_test_df = Y_df[Y_df.ds>'1959-12-31']   # 12 test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>NHITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-01-31</td>\n",
       "      <td>422.038757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-02-29</td>\n",
       "      <td>424.678040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-03-31</td>\n",
       "      <td>439.538879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-04-30</td>\n",
       "      <td>447.967072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1960-05-31</td>\n",
       "      <td>470.603333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_id         ds       NHITS\n",
       "0        1.0 1960-01-31  422.038757\n",
       "1        1.0 1960-02-29  424.678040\n",
       "2        1.0 1960-03-31  439.538879\n",
       "3        1.0 1960-04-30  447.967072\n",
       "4        1.0 1960-05-31  470.603333"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_hat_df = fcst2.predict(df=Y_train_df)\n",
    "Y_hat_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x350 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_series(Y_train_df, Y_hat_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Evaluate Results\n",
    "\n",
    "\n",
    "We evaluate the forecasts of the pre-trained model with the Mean Absolute Error (`mae`).\n",
    "\n",
    "$$\n",
    "\\qquad MAE = \\frac{1}{Horizon} \\sum_{\\tau} |y_{\\tau} - \\hat{y}_{\\tau}|\\qquad\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NHITS     MAE: 17.245\n",
      "ETS       MAE: 16.222\n",
      "AutoARIMA MAE: 18.551\n"
     ]
    }
   ],
   "source": [
    "fcst_mae = mae(Y_test_df.merge(Y_hat_df), models=['NHITS'])['NHITS'].item()\n",
    "print(f'NHITS     MAE: {fcst_mae:.3f}')\n",
    "print('ETS       MAE: 16.222')\n",
    "print('AutoARIMA MAE: 18.551')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
